Update app.py

app.py

@@ -156,30 +156,27 @@ class NeuTTSWrapper:
         """
         [PRODUCER] Runs in a thread, generates audio chunks, and puts them into a queue.
         """
-    try:
-        logger.info("Starting audio production thread...")
-        ref_s = self.tts_model.encode_reference(ref_audio_path)
-        sentences = self._split_text_into_chunks(text)
-        
-        for i, sentence in enumerate(sentences):
-            if not sentence.strip():
-                continue
-            
-            # RESTORED: The per-chunk debug log
-            logger.debug(f"Producing chunk {i+1}/{len(sentences)}: '{sentence[:30]}...'")
-            
-            with torch.no_grad():
-                audio_chunk = self.tts_model.infer(sentence, ref_s, reference_text)
-            
-            # Put the raw audio chunk into the queue for the consumer
-            queue.put_nowait(audio_chunk)
+        try:
+            logger.info("Starting audio production thread...")
+            ref_s = self.tts_model.encode_reference(ref_audio_path)
+            sentences = self._split_text_into_chunks(text)
             
-    except Exception as e:
-        logger.error(f"Error in producer thread: {e}")
-        queue.put_nowait(e)
-    finally:
-        # Signal that production is finished by putting a sentinel value (None)
-        queue.put_nowait(None)
+            for i, sentence in enumerate(sentences):
+                if not sentence.strip():
+                    continue
+                
+                logger.debug(f"Producing chunk {i+1}/{len(sentences)}: '{sentence[:30]}...'")
+                
+                with torch.no_grad():
+                    audio_chunk = self.tts_model.infer(sentence, ref_s, reference_text)
+                
+                queue.put_nowait(audio_chunk)
+                
+        except Exception as e:
+            logger.error(f"Error in producer thread: {e}")
+            queue.put_nowait(e)
+        finally:
+            queue.put_nowait(None)
 
 async def stream_consumer(queue: asyncio.Queue, output_format: str):
     """